Investigating Linguistic Biases in AI Detectors against Non-Native English Scholars from the Global South

As AI-generated text detectors (AI-GTDs) become more widely utilized within academia and research contexts, concerns have been raised over their unintended ramifications due to language biases inherent within these systems. This project seeks to explore these consequences in particular with respect to non-native English-speaking scholars – especially from the Global South, and suggest strategies for creating more inclusive language-related uses of AI.This study notes the difference between plagiarism checkers and AI-GTD. Also, there are plagiarism checkers that have been embedded with AI-GTD. So, I investigate how biased AI-GTD (including plagiarism checkers embedded with AI-GTDs) impact academic progress, research, and global knowledge gaps. It evaluates AI’s false negative flagging of materials written in non-native English language as a potential bottleneck to effective cross-regional communication and diverse expression.I contend that language-limited AI-GTDs limit original contributions - silencing the distinctive voices of non-native scholars in their fields, leading to limited publishing opportunities, funding prospects, and recognition for those whose linguistic norms do not adhere to Western-centric norms. Furthermore, this project stresses the additional disadvantage faced by non-native speakers from Global South regions who often already face complex regional issues necessitating different forms of expressions.To address these challenges, the project emphasizes the need for AI-GTD designers to prioritise linguistic diversity in system development. This involves including an array of linguistic styles and patterns into training datasets in order to increase detectors' abilities to recognize and accommodate diverse voices. By dismantling language barriers and encouraging inclusivity, scholars from diverse backgrounds can contribute to global academic progress unimpeded.